The first step would be to train a couple of different algorithms/classifiers to get a rough idea of how good your results can be without changing your data.
Then, once you do your kmeans, you can use that information to possibly improve your data and then run it through your classifiers again to see if your results improve.
When I say "use that information to possibly improve your data", here's what I mean:
Say your data has 5 features(columns)...see following totally made up data...
It is this data that you ran through your kmeans...using 3 clusters, each row of your data will 'belong' to a cluster...so add it to your data and your data becomes:
2,5,6,3,2,1,0,0 belongs to cluster 1
4,5,2,3,7,0,1,0 belongs to cluster 2
5,8,2,3,5,0,0,1 belongs to cluster 3
You could just add one column and make it 1,2,3 but some algorithms prefer the 0/1 method (one-hot encoding, dummy variable)
See, now you have a bit more information in your data than you started with. Having the before and after comparison models will let you know if it is actually useful information!